Abstract
A variety of distance measures for multivariate time series has been proposed in recent literature. However, evaluations of such measures have been incomplete; comparisons are limited to subsets of similar measures, lacking a holistic view of the field with an appropriate taxonomy of measures. This paper presents a structured evaluation of multivariate time series distance measures. Through a novel taxonomy, measures are categorized based on how they handle the multiple variates; in an atomic or a holistic manner. Experimental evaluation of 12 measures shows that no single measure or approach is superior; the optimal choice depends on the data and the task at hand.
| Original language | English |
|---|---|
| Title of host publication | 2024 IEEE 40th International Conference on Data Engineering Workshops, ICDEW 2024 |
| Pages | 107-112 |
| Number of pages | 6 |
| ISBN (Electronic) | 979-8-3503-8403-1 |
| DOIs | |
| Publication status | Published - 2024 |
Bibliographical note
DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.Funding
This work has received funding from the European Union s Horizon Europe research and innovation programme STELAR under grant agreement No. 101070122.
| Funders | Funder number |
|---|---|
| European Union's Horizon 2020 - Research and Innovation Framework Programme | 101070122 |
Keywords
- Distance Measures
- Multivariate Time Series